Gong, D.Tan, M.Zhang, Y.Van Den Hengel, A.Shi, Q.2018-11-252018-11-252017Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.1934-19402159-53992374-3468http://hdl.handle.net/2440/116283Unlike traditional LASSO enforcing sparsity on the variables, Generalized LASSO (GL) enforces sparsity on a linear transformation of the variables, gaining flexibility and success in many applications. However, many existing GL algorithms do not scale up to high-dimensional problems, and/or only work well for a specific choice of the transformation. We propose an efficient Matching Pursuit Generalized LASSO (MPGL) method, which overcomes these issues, and is guaranteed to converge to a global optimum. We formulate the GL problem as a convex quadratic constrained linear programming (QCLP) problem and tailor-make a cutting plane method. More specifically, our MPGL iteratively activates a subset of nonzero elements of the transformed variables, and solves a subproblem involving only the activated elements thus gaining significant speed-up. Moreover, MPGL is less sensitive to the choice of the trade-off hyper-parameter between data fitting and regularization, and mitigates the long-standing hyper-parameter tuning issue in many existing methods. Experiments demonstrate the superior efficiency and accuracy of the proposed method over the state-of-the-arts in both classification and image processing tasks.enCopyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Conference paper00300772500004856307011362-s2.0-85030029818372167Van Den Hengel, A. [0000-0003-3027-8364]Shi, Q. [0000-0002-9126-2107]